Overview

Brought to you by YData

Dataset statistics

Number of variables58
Number of observations33352
Missing cells18537
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 MiB
Average record size in memory121.7 B

Variable types

Numeric16
Categorical3
Boolean39

Alerts

other_animals has constant value "True" Constant
big_dog_preferences is highly overall correlated with daily_minutes_willing_to_walk_dogs and 10 other fieldsHigh correlation
birth_decade is highly overall correlated with partner_birth_decadeHigh correlation
can_give_medicine is highly overall correlated with cat_skills and 2 other fieldsHigh correlation
cat_skills is highly overall correlated with can_give_medicine and 2 other fieldsHigh correlation
daily_minutes_willing_to_walk_dogs is highly overall correlated with big_dog_preferences and 10 other fieldsHigh correlation
days_since_modified is highly overall correlated with nb_5s_reviews and 4 other fieldsHigh correlation
dog_skills is highly overall correlated with can_give_medicine and 2 other fieldsHigh correlation
has_farm_animal_experience is highly overall correlated with has_poultry_experienceHigh correlation
has_poultry_experience is highly overall correlated with has_farm_animal_experienceHigh correlation
id is highly overall correlated with user_idHigh correlation
interested_in_birds is highly overall correlated with big_dog_preferences and 10 other fieldsHigh correlation
interested_in_cats is highly overall correlated with big_dog_preferences and 10 other fieldsHigh correlation
interested_in_dogs is highly overall correlated with big_dog_preferences and 10 other fieldsHigh correlation
interested_in_farm_animals is highly overall correlated with big_dog_preferences and 10 other fieldsHigh correlation
interested_in_fish is highly overall correlated with big_dog_preferences and 10 other fieldsHigh correlation
interested_in_horses is highly overall correlated with big_dog_preferences and 10 other fieldsHigh correlation
interested_in_poultry is highly overall correlated with big_dog_preferences and 10 other fieldsHigh correlation
interested_in_reptiles is highly overall correlated with big_dog_preferences and 10 other fieldsHigh correlation
interested_in_small_pets is highly overall correlated with big_dog_preferences and 10 other fieldsHigh correlation
nb_5s_reviews is highly overall correlated with days_since_modified and 4 other fieldsHigh correlation
nb_applications is highly overall correlated with days_since_modified and 4 other fieldsHigh correlation
nb_domestic_sits is highly overall correlated with nb_5s_reviews and 3 other fieldsHigh correlation
nb_reviews is highly overall correlated with days_since_modified and 4 other fieldsHigh correlation
nb_sits_completed is highly overall correlated with days_since_modified and 4 other fieldsHigh correlation
partner_birth_decade is highly overall correlated with birth_decadeHigh correlation
sitting_with_another is highly overall correlated with travelling_asHigh correlation
small_dog_preferences is highly overall correlated with big_dog_preferences and 10 other fieldsHigh correlation
travelling_as is highly overall correlated with sitting_with_anotherHigh correlation
user_id is highly overall correlated with idHigh correlation
wish_list_beach is highly overall correlated with wish_list_city and 2 other fieldsHigh correlation
wish_list_city is highly overall correlated with wish_list_beach and 2 other fieldsHigh correlation
wish_list_countryside is highly overall correlated with wish_list_beach and 2 other fieldsHigh correlation
wish_list_mountain is highly overall correlated with wish_list_beach and 2 other fieldsHigh correlation
with_a_baby is highly overall correlated with with_childrenHigh correlation
with_a_child is highly overall correlated with with_childrenHigh correlation
with_a_teen is highly overall correlated with with_childrenHigh correlation
with_a_toddler is highly overall correlated with with_childrenHigh correlation
with_children is highly overall correlated with with_a_baby and 3 other fieldsHigh correlation
years_of_experience is highly overall correlated with can_give_medicine and 3 other fieldsHigh correlation
travelling_as is highly imbalanced (62.9%) Imbalance
with_children is highly imbalanced (65.8%) Imbalance
prev_sitting_experience is highly imbalanced (66.1%) Imbalance
has_dog_experience is highly imbalanced (78.1%) Imbalance
has_cat_experience is highly imbalanced (74.8%) Imbalance
daily_minutes_willing_to_walk_dogs is highly imbalanced (75.3%) Imbalance
happy_to_meet_in_person is highly imbalanced (96.4%) Imbalance
happy_to_video_call is highly imbalanced (99.6%) Imbalance
interested_in_remote_working is highly imbalanced (73.7%) Imbalance
interested_in_dogs is highly imbalanced (52.1%) Imbalance
interested_in_reptiles is highly imbalanced (71.4%) Imbalance
interested_in_horses is highly imbalanced (77.1%) Imbalance
interested_in_fish is highly imbalanced (56.2%) Imbalance
interested_in_poultry is highly imbalanced (63.0%) Imbalance
interested_in_farm_animals is highly imbalanced (73.4%) Imbalance
interested_in_birds is highly imbalanced (65.5%) Imbalance
interested_in_small_pets is highly imbalanced (55.2%) Imbalance
with_a_baby is highly imbalanced (87.6%) Imbalance
with_a_toddler is highly imbalanced (87.1%) Imbalance
with_a_child is highly imbalanced (83.0%) Imbalance
with_a_teen is highly imbalanced (83.4%) Imbalance
big_dog_preferences is highly imbalanced (63.9%) Imbalance
small_dog_preferences is highly imbalanced (58.7%) Imbalance
partner_birth_decade has 18440 (55.3%) missing values Missing
id has unique values Unique
user_id has unique values Unique
nb_reviews has 1682 (5.0%) zeros Zeros
nb_5s_reviews has 2360 (7.1%) zeros Zeros
nb_domestic_sits has 5337 (16.0%) zeros Zeros
nb_local_sits has 23218 (69.6%) zeros Zeros
nb_sits_booked has 29442 (88.3%) zeros Zeros
years_of_experience has 24635 (73.9%) zeros Zeros
nb_of_sitter_pets has 30229 (90.6%) zeros Zeros
five_star_ratio has 2360 (7.1%) zeros Zeros

Reproduction

Analysis started2025-03-20 21:49:10.242744
Analysis finished2025-03-20 21:50:03.568963
Duration53.33 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Unique 

Distinct33352
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1680096.6
Minimum64
Maximum4220986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-20T21:50:03.696373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile119997.15
Q11046443.2
median1897622.5
Q32288758.8
95-th percentile2793209.2
Maximum4220986
Range4220922
Interquartile range (IQR)1242315.5

Descriptive statistics

Standard deviation826648.96
Coefficient of variation (CV)0.49202466
Kurtosis-0.80392209
Mean1680096.6
Median Absolute Deviation (MAD)558566
Skewness-0.51344356
Sum5.6034581 × 1010
Variance6.833485 × 1011
MonotonicityNot monotonic
2025-03-20T21:50:03.901986image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 1
 
< 0.1%
2333094 1
 
< 0.1%
2330156 1
 
< 0.1%
2329729 1
 
< 0.1%
2329379 1
 
< 0.1%
2329218 1
 
< 0.1%
2329189 1
 
< 0.1%
2328986 1
 
< 0.1%
2328813 1
 
< 0.1%
2328577 1
 
< 0.1%
Other values (33342) 33342
> 99.9%
ValueCountFrequency (%)
64 1
< 0.1%
81 1
< 0.1%
86 1
< 0.1%
89 1
< 0.1%
122 1
< 0.1%
123 1
< 0.1%
145 1
< 0.1%
150 1
< 0.1%
160 1
< 0.1%
165 1
< 0.1%
ValueCountFrequency (%)
4220986 1
< 0.1%
4184282 1
< 0.1%
3825369 1
< 0.1%
3775324 1
< 0.1%
3770939 1
< 0.1%
3706519 1
< 0.1%
3660462 1
< 0.1%
3616438 1
< 0.1%
3616365 1
< 0.1%
3591429 1
< 0.1%

user_id
Real number (ℝ)

High correlation  Unique 

Distinct33352
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2737112.1
Minimum116
Maximum7669735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-20T21:50:04.241231image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum116
5-th percentile140302.7
Q11461326.2
median3029934
Q33909177.5
95-th percentile4914570
Maximum7669735
Range7669619
Interquartile range (IQR)2447851.2

Descriptive statistics

Standard deviation1515141.7
Coefficient of variation (CV)0.55355486
Kurtosis-1.034976
Mean2737112.1
Median Absolute Deviation (MAD)1141218.5
Skewness-0.29524881
Sum9.1288162 × 1010
Variance2.2956544 × 1012
MonotonicityNot monotonic
2025-03-20T21:50:04.470644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142 1
 
< 0.1%
4020158 1
 
< 0.1%
4015580 1
 
< 0.1%
4014780 1
 
< 0.1%
4014069 1
 
< 0.1%
4013766 1
 
< 0.1%
4013703 1
 
< 0.1%
4013375 1
 
< 0.1%
4013079 1
 
< 0.1%
4012679 1
 
< 0.1%
Other values (33342) 33342
> 99.9%
ValueCountFrequency (%)
116 1
< 0.1%
142 1
< 0.1%
148 1
< 0.1%
153 1
< 0.1%
195 1
< 0.1%
196 1
< 0.1%
225 1
< 0.1%
231 1
< 0.1%
239 1
< 0.1%
246 1
< 0.1%
ValueCountFrequency (%)
7669735 1
< 0.1%
7597001 1
< 0.1%
6905512 1
< 0.1%
6803051 1
< 0.1%
6794253 1
< 0.1%
6660837 1
< 0.1%
6568260 1
< 0.1%
6484454 1
< 0.1%
6484307 1
< 0.1%
6435647 1
< 0.1%

pct_complete
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.972475
Minimum76
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-03-20T21:50:04.659460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile84
Q184
median84
Q394
95-th percentile94
Maximum100
Range24
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.4155017
Coefficient of variation (CV)0.061559046
Kurtosis-1.2596653
Mean87.972475
Median Absolute Deviation (MAD)0
Skewness0.30554851
Sum2934058
Variance29.327658
MonotonicityNot monotonic
2025-03-20T21:50:04.827854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
84 17949
53.8%
94 12403
37.2%
78 1257
 
3.8%
100 763
 
2.3%
88 565
 
1.7%
90 258
 
0.8%
82 79
 
0.2%
92 38
 
0.1%
76 24
 
0.1%
80 8
 
< 0.1%
Other values (2) 8
 
< 0.1%
ValueCountFrequency (%)
76 24
 
0.1%
78 1257
 
3.8%
80 8
 
< 0.1%
82 79
 
0.2%
84 17949
53.8%
86 4
 
< 0.1%
88 565
 
1.7%
90 258
 
0.8%
92 38
 
0.1%
94 12403
37.2%
ValueCountFrequency (%)
100 763
 
2.3%
98 4
 
< 0.1%
94 12403
37.2%
92 38
 
0.1%
90 258
 
0.8%
88 565
 
1.7%
86 4
 
< 0.1%
84 17949
53.8%
82 79
 
0.2%
80 8
 
< 0.1%

travelling_as
Categorical

High correlation  Imbalance 

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
F
15724 
FM
13406 
M
2550 
FF
 
1192
MM
 
169
Other values (16)
 
311

Length

Max length2
Median length1
Mean length1.4470197
Min length1

Characters and Unicode

Total characters48261
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowF
2nd rowFM
3rd rowFM
4th rowFM
5th rowF

Common Values

ValueCountFrequency (%)
F 15724
47.1%
FM 13406
40.2%
M 2550
 
7.6%
FF 1192
 
3.6%
MM 169
 
0.5%
N 84
 
0.3%
U 46
 
0.1%
FN 34
 
0.1%
X 30
 
0.1%
FX 29
 
0.1%
Other values (11) 88
 
0.3%

Length

2025-03-20T21:50:05.006071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
f 15724
47.1%
fm 13406
40.2%
m 2550
 
7.6%
ff 1192
 
3.6%
mm 169
 
0.5%
n 84
 
0.3%
u 46
 
0.1%
fn 34
 
0.1%
x 30
 
0.1%
fx 29
 
0.1%
Other values (11) 88
 
0.3%

Most occurring characters

ValueCountFrequency (%)
F 31593
65.5%
M 16324
33.8%
N 163
 
0.3%
U 97
 
0.2%
X 71
 
0.1%
T 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48261
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 31593
65.5%
M 16324
33.8%
N 163
 
0.3%
U 97
 
0.2%
X 71
 
0.1%
T 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48261
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 31593
65.5%
M 16324
33.8%
N 163
 
0.3%
U 97
 
0.2%
X 71
 
0.1%
T 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48261
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 31593
65.5%
M 16324
33.8%
N 163
 
0.3%
U 97
 
0.2%
X 71
 
0.1%
T 13
 
< 0.1%

with_children
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
31224 
True
 
2128
ValueCountFrequency (%)
False 31224
93.6%
True 2128
 
6.4%
2025-03-20T21:50:05.165920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

sitting_with_another
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
18515 
True
14837 
ValueCountFrequency (%)
False 18515
55.5%
True 14837
44.5%
2025-03-20T21:50:05.305826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

occupation_type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing90
Missing (%)0.3%
Memory size1.3 MiB
employed
11405 
self-employed
7861 
retired
5645 
working-while-travelling
5468 
taking-time-off
2883 

Length

Max length24
Median length15
Mean length12.248963
Min length7

Characters and Unicode

Total characters407425
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowemployed
2nd rowretired
3rd rowretired
4th rowself-employed
5th rowself-employed

Common Values

ValueCountFrequency (%)
employed 11405
34.2%
self-employed 7861
23.6%
retired 5645
16.9%
working-while-travelling 5468
16.4%
taking-time-off 2883
 
8.6%
(Missing) 90
 
0.3%

Length

2025-03-20T21:50:05.478370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-20T21:50:05.645706image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
employed 11405
34.3%
self-employed 7861
23.6%
retired 5645
17.0%
working-while-travelling 5468
16.4%
taking-time-off 2883
 
8.7%

Most occurring characters

ValueCountFrequency (%)
e 71502
17.5%
l 43531
10.7%
i 27815
 
6.8%
o 27617
 
6.8%
d 24911
 
6.1%
- 24563
 
6.0%
r 22226
 
5.5%
m 22149
 
5.4%
y 19266
 
4.7%
p 19266
 
4.7%
Other values (10) 104579
25.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 407425
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 71502
17.5%
l 43531
10.7%
i 27815
 
6.8%
o 27617
 
6.8%
d 24911
 
6.1%
- 24563
 
6.0%
r 22226
 
5.5%
m 22149
 
5.4%
y 19266
 
4.7%
p 19266
 
4.7%
Other values (10) 104579
25.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 407425
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 71502
17.5%
l 43531
10.7%
i 27815
 
6.8%
o 27617
 
6.8%
d 24911
 
6.1%
- 24563
 
6.0%
r 22226
 
5.5%
m 22149
 
5.4%
y 19266
 
4.7%
p 19266
 
4.7%
Other values (10) 104579
25.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 407425
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 71502
17.5%
l 43531
10.7%
i 27815
 
6.8%
o 27617
 
6.8%
d 24911
 
6.1%
- 24563
 
6.0%
r 22226
 
5.5%
m 22149
 
5.4%
y 19266
 
4.7%
p 19266
 
4.7%
Other values (10) 104579
25.7%

prev_sitting_experience
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
True
31256 
False
 
2096
ValueCountFrequency (%)
True 31256
93.7%
False 2096
 
6.3%
2025-03-20T21:50:05.797512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

other_animals
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
True
33352 
ValueCountFrequency (%)
True 33352
100.0%
2025-03-20T21:50:05.914998image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

has_dog_experience
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
True
32183 
False
 
1169
ValueCountFrequency (%)
True 32183
96.5%
False 1169
 
3.5%
2025-03-20T21:50:06.033536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

has_cat_experience
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
True
31948 
False
 
1404
ValueCountFrequency (%)
True 31948
95.8%
False 1404
 
4.2%
2025-03-20T21:50:06.160543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
25602 
True
7750 
ValueCountFrequency (%)
False 25602
76.8%
True 7750
 
23.2%
2025-03-20T21:50:06.288326image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
25210 
True
8142 
ValueCountFrequency (%)
False 25210
75.6%
True 8142
 
24.4%
2025-03-20T21:50:06.421001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
True
21899 
False
11453 
ValueCountFrequency (%)
True 21899
65.7%
False 11453
34.3%
2025-03-20T21:50:06.547940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

has_poultry_experience
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
18366 
True
14986 
ValueCountFrequency (%)
False 18366
55.1%
True 14986
44.9%
2025-03-20T21:50:06.672195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

has_farm_animal_experience
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
23685 
True
9667 
ValueCountFrequency (%)
False 23685
71.0%
True 9667
29.0%
2025-03-20T21:50:06.795185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
20637 
True
12715 
ValueCountFrequency (%)
False 20637
61.9%
True 12715
38.1%
2025-03-20T21:50:06.923389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
True
21378 
False
11974 
ValueCountFrequency (%)
True 21378
64.1%
False 11974
35.9%
2025-03-20T21:50:07.050239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
True
21178 
False
12174 
ValueCountFrequency (%)
True 21178
63.5%
False 12174
36.5%
2025-03-20T21:50:07.180436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

nb_reviews
Real number (ℝ)

High correlation  Zeros 

Distinct127
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9510974
Minimum0
Maximum175
Zeros1682
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-03-20T21:50:07.347104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q311
95-th percentile32
Maximum175
Range175
Interquartile range (IQR)9

Descriptive statistics

Standard deviation12.162046
Coefficient of variation (CV)1.3587212
Kurtosis17.500087
Mean8.9510974
Median Absolute Deviation (MAD)3
Skewness3.2976869
Sum298537
Variance147.91536
MonotonicityNot monotonic
2025-03-20T21:50:07.571489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6152
18.4%
2 3791
 
11.4%
3 2792
 
8.4%
4 2287
 
6.9%
5 1817
 
5.4%
0 1682
 
5.0%
6 1564
 
4.7%
7 1251
 
3.8%
8 1172
 
3.5%
9 1039
 
3.1%
Other values (117) 9805
29.4%
ValueCountFrequency (%)
0 1682
 
5.0%
1 6152
18.4%
2 3791
11.4%
3 2792
8.4%
4 2287
 
6.9%
5 1817
 
5.4%
6 1564
 
4.7%
7 1251
 
3.8%
8 1172
 
3.5%
9 1039
 
3.1%
ValueCountFrequency (%)
175 1
< 0.1%
168 1
< 0.1%
158 2
< 0.1%
149 1
< 0.1%
137 1
< 0.1%
135 1
< 0.1%
133 1
< 0.1%
129 1
< 0.1%
127 1
< 0.1%
126 1
< 0.1%

nb_5s_reviews
Real number (ℝ)

High correlation  Zeros 

Distinct123
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3440573
Minimum0
Maximum160
Zeros2360
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-03-20T21:50:07.789903image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q310
95-th percentile31
Maximum160
Range160
Interquartile range (IQR)9

Descriptive statistics

Standard deviation11.491595
Coefficient of variation (CV)1.3772191
Kurtosis17.74539
Mean8.3440573
Median Absolute Deviation (MAD)3
Skewness3.3185765
Sum278291
Variance132.05676
MonotonicityNot monotonic
2025-03-20T21:50:08.019740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6224
18.7%
2 3779
11.3%
3 2851
 
8.5%
0 2360
 
7.1%
4 2235
 
6.7%
5 1843
 
5.5%
6 1529
 
4.6%
7 1258
 
3.8%
8 1082
 
3.2%
9 976
 
2.9%
Other values (113) 9215
27.6%
ValueCountFrequency (%)
0 2360
 
7.1%
1 6224
18.7%
2 3779
11.3%
3 2851
8.5%
4 2235
 
6.7%
5 1843
 
5.5%
6 1529
 
4.6%
7 1258
 
3.8%
8 1082
 
3.2%
9 976
 
2.9%
ValueCountFrequency (%)
160 1
< 0.1%
158 1
< 0.1%
157 1
< 0.1%
154 1
< 0.1%
143 1
< 0.1%
130 1
< 0.1%
127 1
< 0.1%
125 1
< 0.1%
124 1
< 0.1%
123 1
< 0.1%

nb_applications
Real number (ℝ)

High correlation 

Distinct858
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.973405
Minimum1
Maximum5388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-03-20T21:50:08.238104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median40
Q396
95-th percentile289
Maximum5388
Range5387
Interquartile range (IQR)80

Descriptive statistics

Standard deviation143.02992
Coefficient of variation (CV)1.7238044
Kurtosis163.68366
Mean82.973405
Median Absolute Deviation (MAD)29
Skewness8.706524
Sum2767329
Variance20457.559
MonotonicityNot monotonic
2025-03-20T21:50:08.430338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 648
 
1.9%
8 633
 
1.9%
9 624
 
1.9%
7 583
 
1.7%
12 577
 
1.7%
11 560
 
1.7%
5 549
 
1.6%
4 546
 
1.6%
10 543
 
1.6%
13 542
 
1.6%
Other values (848) 27547
82.6%
ValueCountFrequency (%)
1 204
 
0.6%
2 393
1.2%
3 451
1.4%
4 546
1.6%
5 549
1.6%
6 648
1.9%
7 583
1.7%
8 633
1.9%
9 624
1.9%
10 543
1.6%
ValueCountFrequency (%)
5388 1
< 0.1%
4233 1
< 0.1%
3826 1
< 0.1%
3608 1
< 0.1%
3145 1
< 0.1%
2993 1
< 0.1%
2974 1
< 0.1%
2845 1
< 0.1%
2772 1
< 0.1%
2582 1
< 0.1%

nb_sits_completed
Real number (ℝ)

High correlation 

Distinct144
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.688025
Minimum0
Maximum194
Zeros44
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-03-20T21:50:08.634000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q313
95-th percentile38
Maximum194
Range194
Interquartile range (IQR)11

Descriptive statistics

Standard deviation14.343207
Coefficient of variation (CV)1.3419886
Kurtosis17.473111
Mean10.688025
Median Absolute Deviation (MAD)4
Skewness3.3346267
Sum356467
Variance205.72759
MonotonicityNot monotonic
2025-03-20T21:50:08.845710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5983
17.9%
2 3731
 
11.2%
3 2795
 
8.4%
4 2348
 
7.0%
5 1859
 
5.6%
6 1605
 
4.8%
7 1306
 
3.9%
8 1191
 
3.6%
9 1093
 
3.3%
10 916
 
2.7%
Other values (134) 10525
31.6%
ValueCountFrequency (%)
0 44
 
0.1%
1 5983
17.9%
2 3731
11.2%
3 2795
8.4%
4 2348
 
7.0%
5 1859
 
5.6%
6 1605
 
4.8%
7 1306
 
3.9%
8 1191
 
3.6%
9 1093
 
3.3%
ValueCountFrequency (%)
194 1
< 0.1%
191 1
< 0.1%
190 1
< 0.1%
180 1
< 0.1%
175 1
< 0.1%
170 1
< 0.1%
159 1
< 0.1%
151 1
< 0.1%
148 2
< 0.1%
143 2
< 0.1%

nb_domestic_sits
Real number (ℝ)

High correlation  Zeros 

Distinct131
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.430379
Minimum0
Maximum188
Zeros5337
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-03-20T21:50:09.045631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q39
95-th percentile30
Maximum188
Range188
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.968117
Coefficient of variation (CV)1.6107008
Kurtosis23.101416
Mean7.430379
Median Absolute Deviation (MAD)3
Skewness3.861458
Sum247818
Variance143.23584
MonotonicityNot monotonic
2025-03-20T21:50:09.362927image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6082
18.2%
0 5337
16.0%
2 3711
11.1%
3 2624
 
7.9%
4 2055
 
6.2%
5 1591
 
4.8%
6 1320
 
4.0%
7 1087
 
3.3%
8 912
 
2.7%
9 857
 
2.6%
Other values (121) 7776
23.3%
ValueCountFrequency (%)
0 5337
16.0%
1 6082
18.2%
2 3711
11.1%
3 2624
7.9%
4 2055
 
6.2%
5 1591
 
4.8%
6 1320
 
4.0%
7 1087
 
3.3%
8 912
 
2.7%
9 857
 
2.6%
ValueCountFrequency (%)
188 1
 
< 0.1%
174 1
 
< 0.1%
170 1
 
< 0.1%
149 1
 
< 0.1%
146 1
 
< 0.1%
135 2
< 0.1%
134 2
< 0.1%
132 1
 
< 0.1%
128 4
< 0.1%
127 2
< 0.1%

nb_local_sits
Real number (ℝ)

Zeros 

Distinct66
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1186735
Minimum0
Maximum83
Zeros23218
Zeros (%)69.6%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-03-20T21:50:09.577796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum83
Range83
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.5316
Coefficient of variation (CV)3.1569532
Kurtosis97.613742
Mean1.1186735
Median Absolute Deviation (MAD)0
Skewness7.9945482
Sum37310
Variance12.472198
MonotonicityNot monotonic
2025-03-20T21:50:09.786966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23218
69.6%
1 4436
 
13.3%
2 1863
 
5.6%
3 1041
 
3.1%
4 656
 
2.0%
5 448
 
1.3%
6 331
 
1.0%
7 254
 
0.8%
8 179
 
0.5%
9 128
 
0.4%
Other values (56) 798
 
2.4%
ValueCountFrequency (%)
0 23218
69.6%
1 4436
 
13.3%
2 1863
 
5.6%
3 1041
 
3.1%
4 656
 
2.0%
5 448
 
1.3%
6 331
 
1.0%
7 254
 
0.8%
8 179
 
0.5%
9 128
 
0.4%
ValueCountFrequency (%)
83 1
< 0.1%
76 1
< 0.1%
74 1
< 0.1%
73 1
< 0.1%
72 1
< 0.1%
71 1
< 0.1%
70 1
< 0.1%
62 1
< 0.1%
60 1
< 0.1%
59 2
< 0.1%

nb_sits_booked
Real number (ℝ)

Zeros 

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24154473
Minimum0
Maximum26
Zeros29442
Zeros (%)88.3%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-03-20T21:50:09.957415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.93168847
Coefficient of variation (CV)3.8572088
Kurtosis85.446611
Mean0.24154473
Median Absolute Deviation (MAD)0
Skewness7.3232242
Sum8056
Variance0.86804341
MonotonicityNot monotonic
2025-03-20T21:50:10.126927image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 29442
88.3%
1 2175
 
6.5%
2 841
 
2.5%
3 393
 
1.2%
4 179
 
0.5%
5 103
 
0.3%
6 78
 
0.2%
7 38
 
0.1%
8 37
 
0.1%
9 18
 
0.1%
Other values (9) 48
 
0.1%
ValueCountFrequency (%)
0 29442
88.3%
1 2175
 
6.5%
2 841
 
2.5%
3 393
 
1.2%
4 179
 
0.5%
5 103
 
0.3%
6 78
 
0.2%
7 38
 
0.1%
8 37
 
0.1%
9 18
 
0.1%
ValueCountFrequency (%)
26 1
 
< 0.1%
22 1
 
< 0.1%
20 1
 
< 0.1%
16 1
 
< 0.1%
14 7
 
< 0.1%
13 5
 
< 0.1%
12 11
< 0.1%
11 8
< 0.1%
10 13
< 0.1%
9 18
0.1%

years_of_experience
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1673063
Minimum0
Maximum10
Zeros24635
Zeros (%)73.9%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-03-20T21:50:10.287808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.9187669
Coefficient of variation (CV)1.8081278
Kurtosis0.027781143
Mean2.1673063
Median Absolute Deviation (MAD)0
Skewness1.3783654
Sum72284
Variance15.356734
MonotonicityNot monotonic
2025-03-20T21:50:10.451721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 24635
73.9%
10 5968
 
17.9%
2 469
 
1.4%
5 468
 
1.4%
3 424
 
1.3%
8 282
 
0.8%
7 276
 
0.8%
6 273
 
0.8%
4 269
 
0.8%
1 180
 
0.5%
ValueCountFrequency (%)
0 24635
73.9%
1 180
 
0.5%
2 469
 
1.4%
3 424
 
1.3%
4 269
 
0.8%
5 468
 
1.4%
6 273
 
0.8%
7 276
 
0.8%
8 282
 
0.8%
9 108
 
0.3%
ValueCountFrequency (%)
10 5968
17.9%
9 108
 
0.3%
8 282
 
0.8%
7 276
 
0.8%
6 273
 
0.8%
5 468
 
1.4%
4 269
 
0.8%
3 424
 
1.3%
2 469
 
1.4%
1 180
 
0.5%

nb_of_sitter_pets
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54956225
Minimum0
Maximum15
Zeros30229
Zeros (%)90.6%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-03-20T21:50:10.605586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0032811
Coefficient of variation (CV)3.6452306
Kurtosis14.207356
Mean0.54956225
Median Absolute Deviation (MAD)0
Skewness3.8852801
Sum18329
Variance4.0131352
MonotonicityNot monotonic
2025-03-20T21:50:10.766490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 30229
90.6%
10 853
 
2.6%
1 431
 
1.3%
2 306
 
0.9%
3 281
 
0.8%
4 273
 
0.8%
5 234
 
0.7%
6 191
 
0.6%
7 189
 
0.6%
9 182
 
0.5%
Other values (6) 183
 
0.5%
ValueCountFrequency (%)
0 30229
90.6%
1 431
 
1.3%
2 306
 
0.9%
3 281
 
0.8%
4 273
 
0.8%
5 234
 
0.7%
6 191
 
0.6%
7 189
 
0.6%
8 162
 
0.5%
9 182
 
0.5%
ValueCountFrequency (%)
15 2
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 2
 
< 0.1%
11 14
 
< 0.1%
10 853
2.6%
9 182
 
0.5%
8 162
 
0.5%
7 189
 
0.6%
6 191
 
0.6%

daily_minutes_willing_to_walk_dogs
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
30483 
120
 
1045
180
 
892
60
 
830
30
 
102

Length

Max length3
Median length1
Mean length1.1440993
Min length1

Characters and Unicode

Total characters38158
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row60
3rd row120
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30483
91.4%
120 1045
 
3.1%
180 892
 
2.7%
60 830
 
2.5%
30 102
 
0.3%

Length

2025-03-20T21:50:10.964126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-20T21:50:11.126687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 30483
91.4%
120 1045
 
3.1%
180 892
 
2.7%
60 830
 
2.5%
30 102
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 33352
87.4%
1 1937
 
5.1%
2 1045
 
2.7%
8 892
 
2.3%
6 830
 
2.2%
3 102
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38158
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 33352
87.4%
1 1937
 
5.1%
2 1045
 
2.7%
8 892
 
2.3%
6 830
 
2.2%
3 102
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38158
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 33352
87.4%
1 1937
 
5.1%
2 1045
 
2.7%
8 892
 
2.3%
6 830
 
2.2%
3 102
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38158
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 33352
87.4%
1 1937
 
5.1%
2 1045
 
2.7%
8 892
 
2.3%
6 830
 
2.2%
3 102
 
0.3%

happy_to_meet_in_person
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
True
33224 
False
 
128
ValueCountFrequency (%)
True 33224
99.6%
False 128
 
0.4%
2025-03-20T21:50:11.256167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

happy_to_video_call
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
True
33343 
False
 
9
ValueCountFrequency (%)
True 33343
> 99.9%
False 9
 
< 0.1%
2025-03-20T21:50:11.375332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

interested_in_remote_working
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
True
31863 
False
 
1489
ValueCountFrequency (%)
True 31863
95.5%
False 1489
 
4.5%
2025-03-20T21:50:11.500418image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

interested_in_dogs
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
29914 
True
3438 
ValueCountFrequency (%)
False 29914
89.7%
True 3438
 
10.3%
2025-03-20T21:50:11.618215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

interested_in_cats
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
29620 
True
3732 
ValueCountFrequency (%)
False 29620
88.8%
True 3732
 
11.2%
2025-03-20T21:50:11.739882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

interested_in_reptiles
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
31685 
True
 
1667
ValueCountFrequency (%)
False 31685
95.0%
True 1667
 
5.0%
2025-03-20T21:50:11.865790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

interested_in_horses
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
32117 
True
 
1235
ValueCountFrequency (%)
False 32117
96.3%
True 1235
 
3.7%
2025-03-20T21:50:11.989921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

interested_in_fish
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
30337 
True
 
3015
ValueCountFrequency (%)
False 30337
91.0%
True 3015
 
9.0%
2025-03-20T21:50:12.121544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

interested_in_poultry
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
30977 
True
 
2375
ValueCountFrequency (%)
False 30977
92.9%
True 2375
 
7.1%
2025-03-20T21:50:12.250362image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

interested_in_farm_animals
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
31843 
True
 
1509
ValueCountFrequency (%)
False 31843
95.5%
True 1509
 
4.5%
2025-03-20T21:50:12.377324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

interested_in_birds
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
31203 
True
 
2149
ValueCountFrequency (%)
False 31203
93.6%
True 2149
 
6.4%
2025-03-20T21:50:12.497932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

interested_in_small_pets
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
30239 
True
3113 
ValueCountFrequency (%)
False 30239
90.7%
True 3113
 
9.3%
2025-03-20T21:50:12.623730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

days_since_modified
Real number (ℝ)

High correlation 

Distinct1202
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean479.85902
Minimum51
Maximum2650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-20T21:50:12.779701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile60
Q1142
median441
Q3804
95-th percentile1018
Maximum2650
Range2599
Interquartile range (IQR)662

Descriptive statistics

Standard deviation343.32808
Coefficient of variation (CV)0.71547698
Kurtosis-1.280916
Mean479.85902
Median Absolute Deviation (MAD)326
Skewness0.27313998
Sum16004258
Variance117874.17
MonotonicityNot monotonic
2025-03-20T21:50:12.981870image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 232
 
0.7%
51 211
 
0.6%
52 205
 
0.6%
72 198
 
0.6%
71 196
 
0.6%
66 194
 
0.6%
74 184
 
0.6%
59 182
 
0.5%
76 181
 
0.5%
77 178
 
0.5%
Other values (1192) 31391
94.1%
ValueCountFrequency (%)
51 211
0.6%
52 205
0.6%
53 169
0.5%
54 166
0.5%
55 146
0.4%
56 130
0.4%
57 144
0.4%
58 171
0.5%
59 182
0.5%
60 157
0.5%
ValueCountFrequency (%)
2650 1
< 0.1%
2289 1
< 0.1%
1879 1
< 0.1%
1873 1
< 0.1%
1831 1
< 0.1%
1828 1
< 0.1%
1758 1
< 0.1%
1658 1
< 0.1%
1654 1
< 0.1%
1649 1
< 0.1%

birth_decade
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1974.2039
Minimum1900
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-20T21:50:13.163591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1950
Q11960
median1980
Q31990
95-th percentile1990
Maximum2000
Range100
Interquartile range (IQR)30

Descriptive statistics

Standard deviation15.427852
Coefficient of variation (CV)0.0078147208
Kurtosis-0.92676163
Mean1974.2039
Median Absolute Deviation (MAD)10
Skewness-0.40153939
Sum65829830
Variance238.01863
MonotonicityNot monotonic
2025-03-20T21:50:13.325255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1990 10041
30.1%
1980 7108
21.3%
1960 5776
17.3%
1970 4650
13.9%
1950 3998
 
12.0%
2000 991
 
3.0%
1940 767
 
2.3%
1930 8
 
< 0.1%
1900 4
 
< 0.1%
1910 2
 
< 0.1%
(Missing) 7
 
< 0.1%
ValueCountFrequency (%)
1900 4
 
< 0.1%
1910 2
 
< 0.1%
1930 8
 
< 0.1%
1940 767
 
2.3%
1950 3998
 
12.0%
1960 5776
17.3%
1970 4650
13.9%
1980 7108
21.3%
1990 10041
30.1%
2000 991
 
3.0%
ValueCountFrequency (%)
2000 991
 
3.0%
1990 10041
30.1%
1980 7108
21.3%
1970 4650
13.9%
1960 5776
17.3%
1950 3998
 
12.0%
1940 767
 
2.3%
1930 8
 
< 0.1%
1910 2
 
< 0.1%
1900 4
 
< 0.1%

partner_birth_decade
Real number (ℝ)

High correlation  Missing 

Distinct9
Distinct (%)0.1%
Missing18440
Missing (%)55.3%
Infinite0
Infinite (%)0.0%
Mean1971.8837
Minimum1900
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-20T21:50:13.482777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1950
Q11960
median1970
Q31990
95-th percentile1990
Maximum2000
Range100
Interquartile range (IQR)30

Descriptive statistics

Standard deviation16.760245
Coefficient of variation (CV)0.008499611
Kurtosis-1.1381522
Mean1971.8837
Median Absolute Deviation (MAD)20
Skewness-0.26434544
Sum29404730
Variance280.9058
MonotonicityNot monotonic
2025-03-20T21:50:13.640344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1990 4079
 
12.2%
1980 2899
 
8.7%
1960 2591
 
7.8%
1950 2442
 
7.3%
1970 1724
 
5.2%
1940 686
 
2.1%
2000 465
 
1.4%
1930 24
 
0.1%
1900 2
 
< 0.1%
(Missing) 18440
55.3%
ValueCountFrequency (%)
1900 2
 
< 0.1%
1930 24
 
0.1%
1940 686
 
2.1%
1950 2442
7.3%
1960 2591
7.8%
1970 1724
5.2%
1980 2899
8.7%
1990 4079
12.2%
2000 465
 
1.4%
ValueCountFrequency (%)
2000 465
 
1.4%
1990 4079
12.2%
1980 2899
8.7%
1970 1724
5.2%
1960 2591
7.8%
1950 2442
7.3%
1940 686
 
2.1%
1930 24
 
0.1%
1900 2
 
< 0.1%

with_a_baby
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
32788 
True
 
564
ValueCountFrequency (%)
False 32788
98.3%
True 564
 
1.7%
2025-03-20T21:50:13.783383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

with_a_toddler
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
32759 
True
 
593
ValueCountFrequency (%)
False 32759
98.2%
True 593
 
1.8%
2025-03-20T21:50:13.897726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

with_a_child
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
32511 
True
 
841
ValueCountFrequency (%)
False 32511
97.5%
True 841
 
2.5%
2025-03-20T21:50:14.016327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

with_a_teen
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
32534 
True
 
818
ValueCountFrequency (%)
False 32534
97.5%
True 818
 
2.5%
2025-03-20T21:50:14.139248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

dog_skills
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
27494 
True
5858 
ValueCountFrequency (%)
False 27494
82.4%
True 5858
 
17.6%
2025-03-20T21:50:14.255945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

cat_skills
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
26800 
True
6552 
ValueCountFrequency (%)
False 26800
80.4%
True 6552
 
19.6%
2025-03-20T21:50:14.376646image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

five_star_ratio
Real number (ℝ)

Zeros 

Distinct431
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87383716
Minimum0
Maximum1
Zeros2360
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-20T21:50:14.556175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.8888889
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.1111111

Descriptive statistics

Standard deviation0.26655722
Coefficient of variation (CV)0.30504221
Kurtosis5.3932562
Mean0.87383716
Median Absolute Deviation (MAD)0
Skewness-2.5478151
Sum29144.217
Variance0.071052745
MonotonicityNot monotonic
2025-03-20T21:50:14.889981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 20663
62.0%
0 2360
 
7.1%
0.5 699
 
2.1%
0.75 676
 
2.0%
0.6666666865 636
 
1.9%
0.8000000119 548
 
1.6%
0.8333333135 524
 
1.6%
0.8571428657 447
 
1.3%
0.875 421
 
1.3%
0.8888888955 393
 
1.2%
Other values (421) 5985
 
17.9%
ValueCountFrequency (%)
0 2360
7.1%
0.125 2
 
< 0.1%
0.1428571492 1
 
< 0.1%
0.1666666716 3
 
< 0.1%
0.200000003 3
 
< 0.1%
0.2222222239 1
 
< 0.1%
0.25 15
 
< 0.1%
0.2727272809 1
 
< 0.1%
0.2857142985 1
 
< 0.1%
0.3333333433 89
 
0.3%
ValueCountFrequency (%)
1 20663
62.0%
0.9908257127 1
 
< 0.1%
0.9900000095 1
 
< 0.1%
0.9898989797 1
 
< 0.1%
0.9893617034 1
 
< 0.1%
0.9891304374 1
 
< 0.1%
0.9888888597 1
 
< 0.1%
0.9887640476 2
 
< 0.1%
0.9880952239 1
 
< 0.1%
0.9876543283 1
 
< 0.1%

can_give_medicine
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
27580 
True
5772 
ValueCountFrequency (%)
False 27580
82.7%
True 5772
 
17.3%
2025-03-20T21:50:15.051451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

big_dog_preferences
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
31065 
True
 
2287
ValueCountFrequency (%)
False 31065
93.1%
True 2287
 
6.9%
2025-03-20T21:50:15.170398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

small_dog_preferences
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
30582 
True
 
2770
ValueCountFrequency (%)
False 30582
91.7%
True 2770
 
8.3%
2025-03-20T21:50:15.297408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

wish_list_city
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
True
26273 
False
7079 
ValueCountFrequency (%)
True 26273
78.8%
False 7079
 
21.2%
2025-03-20T21:50:15.435366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

wish_list_beach
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
True
27342 
False
6010 
ValueCountFrequency (%)
True 27342
82.0%
False 6010
 
18.0%
2025-03-20T21:50:15.567996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

wish_list_mountain
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
True
25589 
False
7763 
ValueCountFrequency (%)
True 25589
76.7%
False 7763
 
23.3%
2025-03-20T21:50:15.694137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

wish_list_countryside
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
True
26009 
False
7343 
ValueCountFrequency (%)
True 26009
78.0%
False 7343
 
22.0%
2025-03-20T21:50:15.820198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Interactions

2025-03-20T21:49:59.854828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:26.280612image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:28.306047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:30.535303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:32.781735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:35.035574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:37.368290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:39.656640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:41.987510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:44.286501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:46.296156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:48.421861image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:50.482274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:52.783996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:55.168622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:57.449849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:59.982088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:26.394208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:28.431550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:30.655674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:32.908737image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:35.167316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:37.493350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:39.789197image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:42.121421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:44.398536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:46.420203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:48.538277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:50.606912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:52.907558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:55.304412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:57.583812image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:50:00.122028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:26.529408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:28.570612image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:30.794078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:33.050870image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:35.345781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:37.638887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:39.951372image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:42.268484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:44.532016image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:46.558225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:48.673926image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:50.745412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:53.053493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:55.460322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:57.760646image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:50:00.251222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:26.652714image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:28.715404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:30.916661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:33.191995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:35.477298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:37.768417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:40.098047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:42.405784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:44.651698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:46.691940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:48.796070image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:50.998224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:53.187698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:55.596944image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2025-03-20T21:49:31.335642image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2025-03-20T21:49:58.643699image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:50:00.962895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2025-03-20T21:49:29.565555image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:31.738533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2025-03-20T21:49:36.362921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2025-03-20T21:49:47.514603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:49.576697image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:51.833123image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:54.144656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:56.460278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:58.922735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:50:01.209762image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:27.551623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:29.694814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:31.995334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:34.180242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:36.502667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:38.839426image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:41.118289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:43.355788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2025-03-20T21:50:01.333337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:27.668984image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:29.823128image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:32.119541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:34.321392image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:36.642538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:38.969641image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:41.260851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:43.488863image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:45.663978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:47.766175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:49.830360image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:52.101517image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:54.433204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:56.731531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:59.198922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:50:01.485148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:27.805193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:29.972616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:32.257515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:34.472342image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:36.796800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:39.115748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2025-03-20T21:49:52.246743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:54.587397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:56.883211image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:59.334602image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:50:01.626774image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:27.929238image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:30.115707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:32.389506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:34.613721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:36.934907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:39.252390image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:41.556232image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:43.768517image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:45.920166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:48.037644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:50.093175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:52.380138image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:54.728999image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:57.029039image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:59.464524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:50:01.759490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:28.055035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:30.257882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:32.521724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2025-03-20T21:49:37.074203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2025-03-20T21:49:43.898604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:46.045040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2025-03-20T21:49:50.225443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:52.514362image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2025-03-20T21:49:57.162010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:59.591759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:50:01.891040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:28.181665image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:30.399545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:32.657519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:34.895694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:37.219090image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:39.523418image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:41.839175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:44.034941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:46.173932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:48.297398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:50.359409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:52.645795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:55.026179image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:57.303830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:49:59.721730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2025-03-20T21:50:16.021405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
big_dog_preferencesbirth_decadecan_give_medicinecat_skillsdaily_minutes_willing_to_walk_dogsdays_since_modifieddog_skillsfive_star_ratiohappy_to_meet_in_personhappy_to_video_callhas_bird_experiencehas_cat_experiencehas_dog_experiencehas_farm_animal_experiencehas_fish_experiencehas_horse_experiencehas_poultry_experiencehas_reptile_experiencehas_small_pet_experienceidinterested_in_birdsinterested_in_catsinterested_in_dogsinterested_in_farm_animalsinterested_in_fishinterested_in_horsesinterested_in_poultryinterested_in_remote_workinginterested_in_reptilesinterested_in_small_petsnb_5s_reviewsnb_applicationsnb_domestic_sitsnb_local_sitsnb_of_sitter_petsnb_reviewsnb_sits_bookednb_sits_completedoccupation_typepartner_birth_decadepct_completeprefers_all_countriesprev_sitting_experiencesitting_with_anothersmall_dog_preferencestravelling_asuser_idwish_list_beachwish_list_citywish_list_countrysidewish_list_mountainwith_a_babywith_a_childwith_a_teenwith_a_toddlerwith_childrenyears_of_experience
big_dog_preferences1.0000.0300.3630.3730.8550.2680.3940.0740.0820.0120.0720.0180.0510.0750.0720.0530.0920.0800.0730.0410.6010.7110.8000.5650.6810.5290.6440.1590.5650.7010.0910.0000.0450.0060.3230.0910.0760.0830.0330.0300.1040.0540.0580.0420.8710.0500.0410.0590.0520.0640.0670.0000.0000.0020.0000.0000.378
birth_decade0.0301.0000.0570.0490.0310.2160.043-0.0110.0190.0180.1100.0410.0590.0620.0580.0790.1460.0670.0820.3510.0460.0600.0510.0360.0530.0280.0540.1830.0470.045-0.296-0.164-0.2310.044-0.022-0.292-0.184-0.2710.3720.877-0.1460.1130.0280.1230.0450.0610.3590.0980.1310.0490.1170.1200.1790.1960.1320.233-0.090
can_give_medicine0.3630.0571.0000.9250.3880.4070.8550.1290.0600.0080.1360.0520.0180.1210.1430.0830.1600.1360.1270.0820.3420.4100.4100.3090.3950.2800.3610.1760.3210.3960.2260.0690.1540.0680.4340.2260.1390.2170.0650.0490.1370.0700.1030.0540.3830.0630.0830.0330.0340.0460.0440.0080.0000.0110.0060.0120.701
cat_skills0.3730.0490.9251.0000.4080.4360.8980.1350.0650.0110.1270.0620.0020.1090.1400.0700.1590.1270.1260.0860.3580.4480.4340.3140.4210.2810.3790.1910.3320.4220.2270.0670.1540.0660.4560.2270.1430.2150.0660.0360.1410.0710.1090.0490.4040.0600.0870.0320.0340.0440.0410.0050.0000.0130.0070.0130.748
daily_minutes_willing_to_walk_dogs0.8550.0310.3880.4081.0000.1490.4180.0400.1230.1010.0700.0130.0520.0750.0690.0510.0930.0660.0710.0340.6440.8060.9050.5810.7560.5330.6900.2080.5710.7730.0530.0000.0290.0120.1760.0530.0470.0500.0290.0300.0600.0500.0650.0370.9350.0230.0340.0610.0540.0650.0620.0040.0000.0030.0000.0000.209
days_since_modified0.2680.2160.4070.4360.1491.0000.4080.0430.0590.0100.1250.0640.0210.1040.1200.0680.1710.0930.1010.1700.2570.3460.3300.2130.3100.1910.2730.2080.2270.315-0.666-0.577-0.453-0.186-0.292-0.670-0.429-0.6490.0880.224-0.1800.0660.1180.0810.2950.0300.1710.0190.0150.0170.0220.0260.0180.0000.0170.024-0.516
dog_skills0.3940.0430.8550.8980.4180.4081.0000.1280.0520.0130.1360.0400.0850.1230.1400.0860.1670.1320.1290.0780.3390.4050.4420.3100.3950.2850.3630.1800.3170.3970.2160.0660.1430.0590.4390.2160.1390.2060.0630.0290.1330.0700.1040.0560.4140.0620.0800.0380.0350.0480.0460.0030.0000.0100.0050.0110.703
five_star_ratio0.074-0.0110.1290.1350.0400.0430.1281.0000.0140.0000.0700.0520.0140.0530.0620.0350.0970.0450.0560.0700.0720.0970.0910.0630.0880.0570.0790.0600.0670.089-0.007-0.195-0.119-0.088-0.005-0.109-0.057-0.1580.058-0.002-0.0240.0050.0790.1020.0820.0350.0670.0230.0230.0150.0200.0250.0250.0200.0240.031-0.025
happy_to_meet_in_person0.0820.0190.0600.0650.1230.0590.0520.0141.0000.0730.0130.0000.0000.0020.0100.0030.0060.0000.0100.0300.0770.1290.1090.0550.1050.0400.0760.0700.0510.1060.0260.0000.0000.0000.0540.0250.0000.0240.0210.0000.0240.0000.0100.0000.1190.0280.0290.0040.0010.0040.0000.0000.0000.0000.0000.0000.076
happy_to_video_call0.0120.0180.0080.0110.1010.0100.0130.0000.0731.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0110.0290.0370.0270.0080.0230.0200.0270.0180.0160.0220.0540.0000.0200.0170.0000.0230.0380.0350.0100.0360.0000.0000.0000.0000.0310.0430.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.027
has_bird_experience0.0720.1100.1360.1270.0700.1250.1360.0700.0130.0061.0000.0780.0760.2760.3610.2210.3220.3230.3560.1170.1660.0670.0720.1080.1040.0970.1080.0550.1180.0980.1180.0730.0820.0350.0660.1190.0530.1240.0870.0750.0930.0460.0550.0480.0740.0520.1160.0480.0230.0660.0630.0100.0150.0100.0160.0230.133
has_cat_experience0.0180.0410.0520.0620.0130.0640.0400.0520.0000.0000.0781.0000.0580.0710.1200.0560.0960.0650.1200.0490.0230.0630.0140.0220.0240.0220.0230.0130.0210.0330.0530.0150.0320.0200.0230.0510.0000.0500.0330.0400.0560.0250.0600.0110.0180.0000.0510.0110.0210.0180.0240.0050.0000.0110.0050.0000.059
has_dog_experience0.0510.0590.0180.0020.0520.0210.0850.0140.0000.0000.0760.0581.0000.0820.0990.0830.0910.0630.0960.0190.0000.0350.0550.0180.0090.0290.0030.0060.0060.0000.0170.0050.0160.0140.0100.0000.0000.0140.0420.0690.0270.0420.0480.0390.0520.0380.0210.0290.0000.0290.0520.0040.0050.0050.0000.0100.019
has_farm_animal_experience0.0750.0620.1210.1090.0750.1040.1230.0530.0020.0000.2760.0710.0821.0000.1990.4990.5200.2140.2500.1070.0900.0530.0660.1730.0690.1520.1200.0410.0860.0780.1060.0500.0720.0000.0590.1050.0380.1070.0660.0490.0710.0330.0570.0660.0650.0690.1050.0190.0340.0720.0670.0140.0400.0270.0310.0450.120
has_fish_experience0.0720.0580.1430.1400.0690.1200.1400.0620.0100.0000.3610.1200.0990.1991.0000.1570.2860.2920.4160.1050.1060.0720.0740.0880.1260.0820.0960.0340.1060.1000.1260.0560.1010.0420.0710.1270.0510.1330.0470.0470.0910.0700.0660.0380.0730.0370.1010.0460.0220.0510.0580.0120.0330.0160.0290.0400.143
has_horse_experience0.0530.0790.0830.0700.0510.0680.0860.0350.0030.0000.2210.0560.0830.4990.1571.0000.3530.1810.1980.1020.0600.0200.0390.1170.0380.1750.0770.0340.0560.0450.0810.0420.0530.0100.0410.0840.0380.0830.0690.0690.0610.0290.0360.0430.0390.0440.0990.0140.0430.0580.0510.0110.0300.0240.0260.0410.088
has_poultry_experience0.0920.1460.1600.1590.0930.1710.1670.0970.0060.0000.3220.0960.0910.5200.2860.3531.0000.2330.3100.1910.1220.0870.0950.1490.1130.1260.1830.0730.1110.1150.1760.0570.1220.0220.0800.1770.0640.1770.1110.1310.1220.0120.0710.1110.0900.1150.1900.0150.0550.0820.0580.0190.0480.0350.0390.0610.175
has_reptile_experience0.0800.0670.1360.1270.0660.0930.1320.0450.0000.0000.3230.0650.0630.2140.2920.1810.2331.0000.2810.0540.1110.0620.0670.1110.0960.1000.0990.0290.1810.0930.1140.0650.0920.0430.0750.1160.0500.1170.0610.0780.0630.0850.0560.0170.0690.0400.0540.0410.0300.0450.0670.0000.0210.0150.0040.0250.138
has_small_pet_experience0.0730.0820.1270.1260.0710.1010.1290.0560.0100.0000.3560.1200.0960.2500.4160.1980.3100.2811.0000.0660.1090.0670.0700.1000.0970.0840.1050.0260.1120.1190.0840.0390.0620.0390.0670.0850.0300.0850.0630.0970.0940.0590.0490.0490.0720.0550.0630.0740.0420.1000.0890.0240.0510.0300.0370.0590.134
id0.0410.3510.0820.0860.0340.1700.0780.0700.0300.0110.1170.0490.0190.1070.1050.1020.1910.0540.0661.0000.0650.0770.0670.0440.0710.0390.0740.1080.0420.067-0.386-0.405-0.266-0.088-0.016-0.390-0.130-0.3950.1400.393-0.2690.0360.0840.0740.0640.0480.9850.2580.2480.2160.2300.0140.0480.0270.0250.041-0.099
interested_in_birds0.6010.0460.3420.3580.6440.2570.3390.0720.0770.0290.1660.0230.0000.0900.1060.0600.1220.1110.1090.0651.0000.7070.6980.6730.7960.5940.7690.1790.7310.7800.1260.0350.0740.0390.2640.1240.0860.1190.0440.0280.1060.0520.0570.0280.6390.0420.0660.0480.0440.0580.0550.0000.0000.0000.0020.0000.355
interested_in_cats0.7110.0600.4100.4480.8060.3460.4050.0970.1290.0370.0670.0630.0350.0530.0720.0200.0870.0620.0670.0770.7071.0000.8890.5830.8460.5250.7430.2190.6160.8650.1400.0250.0820.0360.3440.1390.1070.1290.0620.0450.1300.0450.0730.0190.7930.0380.0770.0510.0530.0550.0460.0100.0080.0130.0000.0170.469
interested_in_dogs0.8000.0510.4100.4340.9050.3300.4420.0910.1090.0270.0720.0140.0550.0660.0740.0390.0950.0670.0700.0670.6980.8891.0000.6090.8270.5570.7480.2100.6130.8480.1200.0110.0690.0220.3510.1200.0970.1120.0530.0380.1260.0450.0710.0350.8880.0430.0660.0590.0510.0610.0550.0000.0070.0110.0000.0110.451
interested_in_farm_animals0.5650.0360.3090.3140.5810.2130.3100.0630.0550.0080.1080.0220.0180.1730.0880.1170.1490.1110.1000.0440.6730.5830.6091.0000.6290.7870.7590.1260.6360.6440.1110.0350.0700.0160.2300.1080.0720.1030.0230.0350.0850.0440.0430.0390.5580.0410.0450.0530.0310.0700.0690.0000.0070.0000.0130.0090.300
interested_in_fish0.6810.0530.3950.4210.7560.3100.3950.0880.1050.0230.1040.0240.0090.0690.1260.0380.1130.0960.0970.0710.7960.8460.8270.6291.0000.5670.7930.1940.7110.8880.1370.0290.0860.0370.3170.1370.1030.1290.0530.0430.1220.0520.0670.0320.7470.0470.0700.0540.0490.0600.0530.0000.0000.0070.0000.0010.425
interested_in_horses0.5290.0280.2800.2810.5330.1910.2850.0570.0400.0200.0970.0220.0290.1520.0820.1750.1260.1000.0840.0390.5940.5250.5570.7870.5671.0000.6580.1130.5780.5760.0910.0400.0590.0190.2100.0880.0580.0830.0240.0350.0780.0410.0400.0310.5110.0330.0390.0500.0320.0640.0660.0000.0060.0000.0090.0090.268
interested_in_poultry0.6440.0540.3610.3790.6900.2730.3630.0790.0760.0270.1080.0230.0030.1200.0960.0770.1830.0990.1050.0740.7690.7430.7480.7590.7930.6581.0000.1790.6880.7970.1380.0300.0840.0270.2830.1370.0930.1290.0470.0430.1140.0420.0570.0460.6750.0560.0750.0540.0380.0710.0640.0000.0000.0000.0120.0020.377
interested_in_remote_working0.1590.1830.1760.1910.2080.2080.1800.0600.0700.0180.0550.0130.0060.0410.0340.0340.0730.0290.0260.1080.1790.2190.2100.1260.1940.1130.1791.0000.1230.1960.0960.0150.0660.0000.1240.0950.0790.0890.2120.1920.0730.0000.0350.0550.1980.0590.1060.0030.0050.0190.0000.0000.0080.0110.0050.0130.229
interested_in_reptiles0.5650.0470.3210.3320.5710.2270.3170.0670.0510.0160.1180.0210.0060.0860.1060.0560.1110.1810.1120.0420.7310.6160.6130.6360.7110.5780.6880.1231.0000.6960.1180.0390.0710.0350.2330.1190.0740.1140.0350.0600.0800.0560.0460.0280.5690.0510.0410.0440.0470.0500.0520.0060.0040.0000.0130.0090.321
interested_in_small_pets0.7010.0450.3960.4220.7730.3150.3970.0890.1060.0220.0980.0330.0000.0780.1000.0450.1150.0930.1190.0670.7800.8650.8480.6440.8880.5760.7970.1960.6961.0000.1290.0230.0770.0350.3250.1280.0960.1190.0460.0340.1240.0540.0680.0310.7630.0450.0670.0550.0530.0630.0540.0000.0000.0060.0030.0000.432
nb_5s_reviews0.091-0.2960.2260.2270.053-0.6660.216-0.0070.0260.0540.1180.0530.0170.1060.1260.0810.1760.1140.084-0.3860.1260.1400.1200.1110.1370.0910.1380.0960.1180.1291.0000.8220.7160.3160.1780.9890.3950.9620.087-0.3090.2430.0270.0760.0470.1090.020-0.3810.0770.0680.0570.0650.0320.0280.0200.0240.0490.362
nb_applications0.000-0.1640.0690.0670.000-0.5770.066-0.1950.0000.0000.0730.0150.0050.0500.0560.0420.0570.0650.039-0.4050.0350.0250.0110.0350.0290.0400.0300.0150.0390.0230.8221.0000.6380.3060.1580.8410.3470.8640.024-0.1820.2390.0320.0150.0320.0110.010-0.3960.0240.0170.0260.0210.0000.0000.0020.0140.0000.327
nb_domestic_sits0.045-0.2310.1540.1540.029-0.4530.143-0.1190.0000.0200.0820.0320.0160.0720.1010.0530.1220.0920.062-0.2660.0740.0820.0690.0700.0860.0590.0840.0660.0710.0770.7160.6381.0000.4310.0880.7250.3040.7540.060-0.2470.1280.0080.0560.0120.0570.000-0.2610.0580.0570.0460.0510.0280.0200.0130.0130.0360.217
nb_local_sits0.0060.0440.0680.0660.012-0.1860.059-0.0880.0000.0170.0350.0200.0140.0000.0420.0100.0220.0430.039-0.0880.0390.0360.0220.0160.0370.0190.0270.0000.0350.0350.3160.3060.4311.0000.0520.3230.1160.3450.0220.0490.0650.0130.0260.0530.0170.025-0.0830.0310.0110.0360.0360.0050.0080.0120.0070.0270.098
nb_of_sitter_pets0.323-0.0220.4340.4560.176-0.2920.439-0.0050.0540.0000.0660.0230.0100.0590.0710.0410.0800.0750.067-0.0160.2640.3440.3510.2300.3170.2100.2830.1240.2330.3250.1780.1580.0880.0521.0000.1790.1470.1680.022-0.0160.1030.0530.0710.0630.3460.026-0.0170.0410.0390.0410.0400.0000.0260.0000.0000.0170.487
nb_reviews0.091-0.2920.2260.2270.053-0.6700.216-0.1090.0250.0230.1190.0510.0000.1050.1270.0840.1770.1160.085-0.3900.1240.1390.1200.1080.1370.0880.1370.0950.1190.1280.9890.8410.7250.3230.1791.0000.3930.9750.088-0.3040.2440.0270.0770.0390.1080.017-0.3840.0770.0680.0560.0670.0330.0280.0190.0230.0490.363
nb_sits_booked0.076-0.1840.1390.1430.047-0.4290.139-0.0570.0000.0380.0530.0000.0000.0380.0510.0380.0640.0500.030-0.1300.0860.1070.0970.0720.1030.0580.0930.0790.0740.0960.3950.3470.3040.1160.1470.3931.0000.3850.045-0.1910.1040.0130.0220.0260.0860.000-0.1300.0000.0000.0000.0000.0070.0000.0350.0000.0270.257
nb_sits_completed0.083-0.2710.2170.2150.050-0.6490.206-0.1580.0240.0350.1240.0500.0140.1070.1330.0830.1770.1170.085-0.3950.1190.1290.1120.1030.1290.0830.1290.0890.1140.1190.9620.8640.7540.3450.1680.9750.3851.0000.084-0.2880.2420.0230.0790.0190.0990.015-0.3890.0830.0730.0650.0760.0320.0290.0230.0220.0480.350
occupation_type0.0330.3720.0650.0660.0290.0880.0630.0580.0210.0100.0870.0330.0420.0660.0470.0690.1110.0610.0630.1400.0440.0620.0530.0230.0530.0240.0470.2120.0350.0460.0870.0240.0600.0220.0220.0880.0450.0841.0000.3600.0670.0770.0320.1670.0480.0970.1400.0620.0900.0030.0730.0580.0650.0680.0550.1100.055
partner_birth_decade0.0300.8770.0490.0360.0300.2240.029-0.0020.0000.0360.0750.0400.0690.0490.0470.0690.1310.0780.0970.3930.0280.0450.0380.0350.0430.0350.0430.1920.0600.034-0.309-0.182-0.2470.049-0.016-0.304-0.191-0.2880.3601.000-0.1360.1140.0160.0090.0350.0810.4010.1270.1470.0730.1490.1850.2410.2160.2040.307-0.079
pct_complete0.104-0.1460.1370.1410.060-0.1800.133-0.0240.0240.0000.0930.0560.0270.0710.0910.0610.1220.0630.094-0.2690.1060.1300.1260.0850.1220.0780.1140.0730.0800.1240.2430.2390.1280.0650.1030.2440.1040.2420.067-0.1361.0000.0590.0850.0770.1200.192-0.2660.0950.0790.0730.0890.0000.0000.0000.0090.0050.148
prefers_all_countries0.0540.1130.0700.0710.0500.0660.0700.0050.0000.0000.0460.0250.0420.0330.0700.0290.0120.0850.0590.0360.0520.0450.0450.0440.0520.0410.0420.0000.0560.0540.0270.0320.0080.0130.0530.0270.0130.0230.0770.1140.0591.0000.0270.0120.0480.0120.0410.1010.0880.0890.1320.0000.0000.0050.0060.0000.071
prev_sitting_experience0.0580.0280.1030.1090.0650.1180.1040.0790.0100.0000.0550.0600.0480.0570.0660.0360.0710.0560.0490.0840.0570.0730.0710.0430.0670.0400.0570.0350.0460.0680.0760.0150.0560.0260.0710.0770.0220.0790.0320.0160.0850.0271.0000.0000.0640.0090.0830.0090.0000.0120.0230.0000.0110.0000.0000.0030.132
sitting_with_another0.0420.1230.0540.0490.0370.0810.0560.1020.0000.0000.0480.0110.0390.0660.0380.0430.1110.0170.0490.0740.0280.0190.0350.0390.0320.0310.0460.0550.0280.0310.0470.0320.0120.0530.0630.0390.0260.0190.1670.0090.0770.0120.0001.0000.0360.9950.0700.0000.0290.0470.0310.1030.0600.0270.0800.1140.057
small_dog_preferences0.8710.0450.3830.4040.9350.2950.4140.0820.1190.0310.0740.0180.0520.0650.0730.0390.0900.0690.0720.0640.6390.7930.8880.5580.7470.5110.6750.1980.5690.7630.1090.0110.0570.0170.3460.1080.0860.0990.0480.0350.1200.0480.0640.0361.0000.0440.0640.0590.0540.0600.0570.0000.0030.0100.0000.0060.411
travelling_as0.0500.0610.0630.0600.0230.0300.0620.0350.0280.0430.0520.0000.0380.0690.0370.0440.1150.0400.0550.0480.0420.0380.0430.0410.0470.0330.0560.0590.0510.0450.0200.0100.0000.0250.0260.0170.0000.0150.0970.0810.1920.0120.0090.9950.0441.0000.0530.0110.0420.0500.0370.1100.0610.0320.0840.1240.027
user_id0.0410.3590.0830.0870.0340.1710.0800.0670.0290.0120.1160.0510.0210.1050.1010.0990.1900.0540.0630.9850.0660.0770.0660.0450.0700.0390.0750.1060.0410.067-0.381-0.396-0.261-0.083-0.017-0.384-0.130-0.3890.1400.401-0.2660.0410.0830.0700.0640.0531.0000.2180.2130.1750.1960.0170.0460.0230.0220.036-0.099
wish_list_beach0.0590.0980.0330.0320.0610.0190.0380.0230.0040.0000.0480.0110.0290.0190.0460.0140.0150.0410.0740.2580.0480.0510.0590.0530.0540.0500.0540.0030.0440.0550.0770.0240.0580.0310.0410.0770.0000.0830.0620.1270.0950.1010.0090.0000.0590.0110.2181.0000.6370.7080.7180.0000.0200.0070.0080.0170.040
wish_list_city0.0520.1310.0340.0340.0540.0150.0350.0230.0010.0000.0230.0210.0000.0340.0220.0430.0550.0300.0420.2480.0440.0530.0510.0310.0490.0320.0380.0050.0470.0530.0680.0170.0570.0110.0390.0680.0000.0730.0900.1470.0790.0880.0000.0290.0540.0420.2130.6371.0000.5300.5340.0220.0000.0040.0070.0150.042
wish_list_countryside0.0640.0490.0460.0440.0650.0170.0480.0150.0040.0000.0660.0180.0290.0720.0510.0580.0820.0450.1000.2160.0580.0550.0610.0700.0600.0640.0710.0190.0500.0630.0570.0260.0460.0360.0410.0560.0000.0650.0030.0730.0730.0890.0120.0470.0600.0500.1750.7080.5301.0000.7710.0070.0260.0120.0130.0290.049
wish_list_mountain0.0670.1170.0440.0410.0620.0220.0460.0200.0000.0000.0630.0240.0520.0670.0580.0510.0580.0670.0890.2300.0550.0460.0550.0690.0530.0660.0640.0000.0520.0540.0650.0210.0510.0360.0400.0670.0000.0760.0730.1490.0890.1320.0230.0310.0570.0370.1960.7180.5340.7711.0000.0070.0190.0070.0110.0230.045
with_a_baby0.0000.1200.0080.0050.0040.0260.0030.0250.0000.0000.0100.0050.0040.0140.0120.0110.0190.0000.0240.0140.0000.0100.0000.0000.0000.0000.0000.0000.0060.0000.0320.0000.0280.0050.0000.0330.0070.0320.0580.1850.0000.0000.0000.1030.0000.1100.0170.0000.0220.0070.0071.0000.0940.0370.2720.5020.013
with_a_child0.0000.1790.0000.0000.0000.0180.0000.0250.0000.0000.0150.0000.0050.0400.0330.0300.0480.0210.0510.0480.0000.0080.0070.0070.0000.0060.0000.0080.0040.0000.0280.0000.0200.0080.0260.0280.0000.0290.0650.2410.0000.0000.0110.0600.0030.0610.0460.0200.0000.0260.0190.0941.0000.2590.3410.6160.000
with_a_teen0.0020.1960.0110.0130.0030.0000.0100.0200.0000.0000.0100.0110.0050.0270.0160.0240.0350.0150.0300.0270.0000.0130.0110.0000.0070.0000.0000.0110.0000.0060.0200.0020.0130.0120.0000.0190.0350.0230.0680.2160.0000.0050.0000.0270.0100.0320.0230.0070.0040.0120.0070.0370.2591.0000.0910.6070.000
with_a_toddler0.0000.1320.0060.0070.0000.0170.0050.0240.0000.0000.0160.0050.0000.0310.0290.0260.0390.0040.0370.0250.0020.0000.0000.0130.0000.0090.0120.0050.0130.0030.0240.0140.0130.0070.0000.0230.0000.0220.0550.2040.0090.0060.0000.0800.0000.0840.0220.0080.0070.0130.0110.2720.3410.0911.0000.5150.000
with_children0.0000.2330.0120.0130.0000.0240.0110.0310.0000.0000.0230.0000.0100.0450.0400.0410.0610.0250.0590.0410.0000.0170.0110.0090.0010.0090.0020.0130.0090.0000.0490.0000.0360.0270.0170.0490.0270.0480.1100.3070.0050.0000.0030.1140.0060.1240.0360.0170.0150.0290.0230.5020.6160.6070.5151.0000.000
years_of_experience0.378-0.0900.7010.7480.209-0.5160.703-0.0250.0760.0270.1330.0590.0190.1200.1430.0880.1750.1380.134-0.0990.3550.4690.4510.3000.4250.2680.3770.2290.3210.4320.3620.3270.2170.0980.4870.3630.2570.3500.055-0.0790.1480.0710.1320.0570.4110.027-0.0990.0400.0420.0490.0450.0130.0000.0000.0000.0001.000

Missing values

2025-03-20T21:50:02.218536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-20T21:50:02.817191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-20T21:50:03.440153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

iduser_idpct_completetravelling_aswith_childrensitting_with_anotheroccupation_typeprev_sitting_experienceother_animalshas_dog_experiencehas_cat_experiencehas_reptile_experiencehas_horse_experiencehas_fish_experiencehas_poultry_experiencehas_farm_animal_experiencehas_bird_experiencehas_small_pet_experienceprefers_all_countriesnb_reviewsnb_5s_reviewsnb_applicationsnb_sits_completednb_domestic_sitsnb_local_sitsnb_sits_bookedyears_of_experiencenb_of_sitter_petsdaily_minutes_willing_to_walk_dogshappy_to_meet_in_personhappy_to_video_callinterested_in_remote_workinginterested_in_dogsinterested_in_catsinterested_in_reptilesinterested_in_horsesinterested_in_fishinterested_in_poultryinterested_in_farm_animalsinterested_in_birdsinterested_in_small_petsdays_since_modifiedbirth_decadepartner_birth_decadewith_a_babywith_a_toddlerwith_a_childwith_a_teendog_skillscat_skillsfive_star_ratiocan_give_medicinebig_dog_preferencessmall_dog_preferenceswish_list_citywish_list_beachwish_list_mountainwish_list_countryside
08114294FFalseFalseemployedTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueFalseFalse11918863421401020TrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse2001960<NA>FalseFalseFalseFalseTrueTrue0.818182TrueFalseFalseFalseFalseFalseFalse
18614894FMFalseTrueretiredTrueTrueTrueTrueFalseFalseTrueTrueFalseTrueTrueTrue57573356061310060TrueTrueTrueTrueTrueFalseFalseTrueTrueFalseTrueTrue7519501950FalseFalseFalseFalseTrueTrue1.000000TrueFalseTrueTrueTrueTrueTrue
28915388FMFalseTrueretiredTrueTrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalse876314120100120TrueTrueFalseTrueTrueFalseFalseTrueTrueFalseFalseTrue22219401940FalseFalseFalseFalseFalseFalse0.875000FalseTrueTrueFalseFalseFalseFalse
312319694FMFalseTrueself-employedTrueTrueTrueTrueFalseTrueTrueTrueTrueFalseTrueTrue99761111001000TrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse10419501940FalseFalseFalseFalseTrueTrue1.000000TrueFalseFalseTrueTrueTrueTrue
4429739894FFalseFalseself-employedTrueTrueTrueTrueTrueTrueTrueTrueTrueFalseFalseTrue2117390261800000TrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse5241950<NA>FalseFalseFalseFalseFalseFalse0.809524FalseFalseFalseFalseTrueTrueFalse
538967684FMFalseTrueworking-while-travellingTrueTrueTrueTrueFalseFalseTrueTrueFalseFalseFalseFalse15135216200000TrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse77519501950FalseFalseFalseFalseFalseFalse0.866667FalseFalseFalseFalseFalseFalseFalse
639268284FFalseFalseself-employedTrueTrueTrueTrueTrueFalseTrueTrueTrueTrueTrueFalse64593598176110000TrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse7321970<NA>FalseFalseFalseFalseFalseFalse0.921875FalseFalseFalseFalseFalseFalseFalse
739468594FMFalseTrueretiredTrueTrueTrueTrueFalseTrueTrueFalseFalseFalseFalseTrue4342365471901000TrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse10419501950FalseFalseFalseFalseFalseFalse0.976744FalseFalseFalseFalseFalseFalseFalse
83620569988FFalseFalseretiredTrueTrueTrueTrueFalseFalseFalseFalseFalseFalseFalseTrue4138175454221000TrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse2041940<NA>FalseFalseFalseFalseFalseFalse0.926829FalseFalseFalseFalseFalseFalseFalse
96661255100FMFalseTrueretiredTrueTrueTrueTrueFalseTrueTrueTrueTrueFalseTrueTrue76837201100120TrueTrueFalseTrueTrueFalseTrueTrueTrueTrueFalseTrue19619501950FalseFalseFalseFalseTrueTrue0.857143TrueTrueTrueTrueTrueTrueTrue
iduser_idpct_completetravelling_aswith_childrensitting_with_anotheroccupation_typeprev_sitting_experienceother_animalshas_dog_experiencehas_cat_experiencehas_reptile_experiencehas_horse_experiencehas_fish_experiencehas_poultry_experiencehas_farm_animal_experiencehas_bird_experiencehas_small_pet_experienceprefers_all_countriesnb_reviewsnb_5s_reviewsnb_applicationsnb_sits_completednb_domestic_sitsnb_local_sitsnb_sits_bookedyears_of_experiencenb_of_sitter_petsdaily_minutes_willing_to_walk_dogshappy_to_meet_in_personhappy_to_video_callinterested_in_remote_workinginterested_in_dogsinterested_in_catsinterested_in_reptilesinterested_in_horsesinterested_in_fishinterested_in_poultryinterested_in_farm_animalsinterested_in_birdsinterested_in_small_petsdays_since_modifiedbirth_decadepartner_birth_decadewith_a_babywith_a_toddlerwith_a_childwith_a_teendog_skillscat_skillsfive_star_ratiocan_give_medicinebig_dog_preferencessmall_dog_preferenceswish_list_citywish_list_beachwish_list_mountainwish_list_countryside
349063276279588319584MMFalseTrueemployedTrueTrueTrueTrueFalseTrueTrueTrueTrueFalseTrueTrue441205510000TrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse53219901990FalseFalseFalseFalseFalseFalse1.000000FalseFalseFalseTrueTrueTrueTrue
349073276738588402294FFalseFalseretiredTrueTrueFalseTrueFalseFalseTrueFalseFalseTrueTrueTrue1714581717011000TrueTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalse551950<NA>FalseFalseFalseFalseFalseTrue0.823529TrueFalseFalseFalseTrueFalseTrue
349083295400591097284FMFalseTrueemployedTrueTrueTrueTrueFalseFalseTrueTrueFalseFalseTrueTrue1121000000TrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse65919801980FalseFalseFalseFalseFalseFalse1.000000FalseFalseFalseTrueTrueTrueTrue
349093337921597862284FMFalseTrueemployedTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueFalse1151100000TrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse48919801970FalseFalseFalseFalseFalseFalse1.000000FalseFalseFalseTrueTrueTrueTrue
349103391830606801694FFalseFalseemployedTrueTrueTrueTrueTrueFalseTrueFalseFalseFalseTrueTrue33516640000TrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse5651990<NA>FalseFalseFalseFalseFalseFalse1.000000FalseFalseFalseFalseTrueTrueTrue
349113408537609834984MFalseFalseworking-while-travellingTrueTrueTrueTrueFalseFalseFalseFalseFalseTrueFalseTrue1041000000TrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse6421960<NA>FalseFalseFalseFalseFalseFalse0.000000FalseFalseFalseTrueTrueTrueTrue
349123517181629376984FMFalseTrueretiredTrueTrueTrueTrueFalseFalseTrueTrueFalseTrueTrueFalse109401212011000TrueTrueTrueFalseTrueFalseFalseTrueFalseFalseFalseTrue12419401940FalseFalseFalseFalseTrueTrue0.900000FalseFalseFalseTrueTrueTrueTrue
349133546308635081584FFalseFalseself-employedTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueFalse443155001000TrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse2771970<NA>FalseFalseFalseFalseTrueTrue1.000000TrueFalseFalseTrueTrueTrueTrue
349143591429643564784MUFalseTruetaking-time-offTrueTrueTrueTrueFalseFalseTrueTrueTrueFalseFalseTrue19191052221001000TrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse10619901980FalseFalseFalseFalseFalseFalse1.000000FalseFalseFalseFalseFalseFalseFalse
349153770939679425384FMTrueTrueemployedTrueTrueTrueTrueFalseTrueFalseFalseFalseFalseTrueFalse11310001070TrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse59519901980TrueFalseFalseFalseTrueTrue1.000000TrueFalseFalseTrueTrueTrueTrue